Investigation and performance enhancement of the empirical mode decomposition method based on a heuristic search
Investigation and performance enhancement of the empirical mode decomposition method based on a heuristic search
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DOI：
10.1109/tsp.2007.901155
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发表时间：
2008-01-01
影响因子：
5.4
通讯作者：
McLaughlin, Steve
中科院分区：
文献类型：
--
作者：
Kopsinis, Yannis;McLaughlin, Steve
经验模态分解（EMD）是一种相对较新的、数据驱动的自适应多分量信号分析技术。尽管它有许多有趣的特征，并且经常表现出分解非线性和非平稳信号的能力，但它缺乏强有力的理论基础，无法进行性能分析，从而以系统的方式增强和优化方法。本文尝试以另一种方式对EMD进行优化。使用特殊定义的多分量信号，可以提前知道最优输出，并在遗传算法框架内用于优化无emd参数。本文的贡献是双重的。首先，优化插值点和分段插值多项式形成信号的上下包络，揭示了该方法以前隐藏的重要特征。其次，提出了优化参数估计的基本方向，从而显著提高了性能。
Empirical mode decomposition (EMD) is a relatively new, data-driven adaptive technique for analyzing multicomponent signals. Although it has many interesting features and often exhibits an ability to decompose nonlinear and nonstationary signals, it lacks a strong theoretical basis which would allow a performance analysis and hence the enhancement and optimization of the method in a systematic way. In this paper, the optimization of EMD is attempted in an alternative manner. Using specially defined multicomponent signals, the optimum outputs can be known in advance and used in the optimization of the EMD-free parameters within a genetic algorithm framework. The contributions of this paper are two-fold. First, the optimization of both the interpolation points and the piecewise interpolating polynomials for the formation of the upper and lower envelopes of the signal reveal important characteristics of the method which where previously hidden. Second, basic directions for the estimates of the optimized parameters are developed, leading to significant performance improvements.